{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "amended-composer",
   "metadata": {},
   "source": [
    "框架\n",
    "\n",
    "    一、导入数据\n",
    "    二、原始序列的检验\n",
    "    三、一阶差分序列的检验\n",
    "    四、定阶（参数调优)\n",
    "    五、建模与预测\n",
    "    \n",
    "    参考材料：《Python数据分析与挖掘实践》"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quarterly-measurement",
   "metadata": {},
   "source": [
    "## 工具准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "short-partner",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import json\n",
    "import itertools\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "from matplotlib.pylab import style #自定义图表风格\n",
    "style.use('ggplot')\n",
    "\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']\n",
    "# 解决坐标轴刻度负号乱码\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "#pip install statsmodels\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf  #自相关图、偏自相关图\n",
    "from statsmodels.tsa.stattools import adfuller as ADF #平稳性检验\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox #白噪声检验\n",
    "import statsmodels.api as sm #D-W检验,一阶自相关检验\n",
    "from statsmodels.graphics.api import qqplot #画QQ图,检验一组数据是否服从正态分布\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-metabolism",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "liquid-listening",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../saleByTopFromSeries.json','r',encoding='utf-8') as f:\n",
    "    listRS = json.load(f)\n",
    "index = 200\n",
    "pyearAndMonth = listRS[index][\"yearAndMonth\"]\n",
    "yearAndMonth = pyearAndMonth[:36]\n",
    "psalesData= listRS[index][\"salesData\"]\n",
    "salesData = psalesData[:36]\n",
    "series = listRS[index][\"series\"]\n",
    "seriesId = listRS[index][\"seriesId\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "mounted-bearing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建字典对象\n",
    "pdata = {\"date\":pyearAndMonth,\"sale\":psalesData}\n",
    "\n",
    "# 创建dataframe\n",
    "psale = pd.DataFrame(pdata)\n",
    "\n",
    "# # 转换日期数据类型\n",
    "# index = pd.DatetimeIndex(yearAndMonth)\n",
    "\n",
    "# 将日期设置索引值\n",
    "psale.set_index(['date'],inplace=True)\n",
    "\n",
    "# 转换销售量数据类型\n",
    "psale.sale=psale.sale.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "decreased-retreat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 历史数据长度\n",
    "g_datalen = len(salesData)\n",
    "    \n",
    "\n",
    "g_datalen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sonic-breakdown",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 36 entries, 2017/10 to 2020/9\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   sale    36 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 576.0+ bytes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017/10</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017/11</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017/12</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/1</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/2</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sale\n",
       "date         \n",
       "2017/10   0.0\n",
       "2017/11   0.0\n",
       "2017/12   0.0\n",
       "2018/1    0.0\n",
       "2018/2    0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建字典对象\n",
    "data = {\"date\":yearAndMonth,\"sale\":salesData}\n",
    "\n",
    "# 创建dataframe\n",
    "sale = pd.DataFrame(data)\n",
    "\n",
    "# # 转换日期数据类型\n",
    "# index = pd.DatetimeIndex(yearAndMonth)\n",
    "\n",
    "# 将日期设置索引值\n",
    "sale.set_index(['date'],inplace=True)\n",
    "\n",
    "# 转换销售量数据类型\n",
    "sale.sale=sale.sale.astype('float')\n",
    "\n",
    "primitiveSale = sale\n",
    "\n",
    "# 查看数据信息|\n",
    "sale.info()\n",
    "sale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "searching-assistant",
   "metadata": {},
   "source": [
    "## 原始序列的检验"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "asian-poison",
   "metadata": {},
   "source": [
    "#### 单位根检验，满足该条件，则拒绝原假设（非平稳序列），说明是平稳序列\n",
    "ADF(sale.sale)[1]<0.05\n",
    "#### 白噪声检验，满足该条件，则拒绝原假设（纯随机序列），说明是非白噪声\n",
    "acorr_ljungbox(sale,lags=1)['lb_pvalue'].values[0]<0.05\n",
    "#### 进行差分运算\n",
    "sale=sale.diff(periods=1, axis=0).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "separate-chase",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------第1次计算---------------------:\n",
      " 单位根检验： (-1.4229710732877874, 0.571215307737125, 0, 35, {'1%': -3.6327426647230316, '5%': -2.9485102040816327, '10%': -2.6130173469387756}, 8.076985957939819) \n",
      "白噪声检验：      lb_stat     lb_pvalue\n",
      "1  24.343037  8.061762e-07\n",
      "---------------------第2次计算---------------------:\n",
      " 单位根检验： (-8.464890760642392, 1.531940288100946e-13, 0, 34, {'1%': -3.639224104416853, '5%': -2.9512301791166293, '10%': -2.614446989619377}, 11.36729143038442) \n",
      "白噪声检验：     lb_stat  lb_pvalue\n",
      "1  5.687409   0.017087\n",
      "\n",
      "经历了1阶差分\n"
     ]
    }
   ],
   "source": [
    "d = 0\n",
    "while(True):\n",
    "    print(\"---------------------第{}次计算---------------------:\\n\".format(str(d+1)),\n",
    "          \"单位根检验：\",ADF(sale.sale),\"\\n白噪声检验：\",acorr_ljungbox(sale,lags=1))\n",
    "    if (ADF(sale.sale)[1]<0.05) and (acorr_ljungbox(sale,lags=1)['lb_pvalue'].values[0]<0.05):\n",
    "        break\n",
    "    else:\n",
    "        d = d+1\n",
    "        sale=sale.diff(periods=1, axis=0).dropna()\n",
    "print(\"\\n经历了{}阶差分\".format(str(d)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "designing-excitement",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(p, q) =(sm.tsa.arma_order_select_ic(primitiveSale,max_ar=3,max_ma=3,ic='aic')['aic_min_order'])\n",
    "(p, q) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "authentic-eight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x238e24d9fd0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "<matplotlib.legend.Legend at 0x238e28b64a8>"
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     "execution_count": 8,
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     "execution_count": 8,
     "metadata": {},
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    {
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       "[<matplotlib.lines.Line2D at 0x238e3821a20>]"
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     "execution_count": 8,
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    {
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       "<matplotlib.legend.Legend at 0x238e383c400>"
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     "execution_count": 8,
     "metadata": {},
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       "<AxesSubplot:>"
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     "execution_count": 8,
     "metadata": {},
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       "[<matplotlib.lines.Line2D at 0x238e3864908>]"
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     "execution_count": 8,
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       "<matplotlib.legend.Legend at 0x238e38934e0>"
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     "execution_count": 8,
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     "execution_count": 8,
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     "execution_count": 8,
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     },
     "execution_count": 8,
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "primitiveSale.index = pd.DatetimeIndex(primitiveSale.index)\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "decomposition = seasonal_decompose(primitiveSale)\n",
    "trend = decomposition.trend #趋势效应\n",
    "seasonal = decomposition.seasonal #季节效应\n",
    "residual = decomposition.resid #随机效应\n",
    "plt.subplot(411)\n",
    "plt.plot(primitiveSale, label=u'原始数据')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label=u'趋势')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal,label=u'季节性')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label=u'残差')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "infrared-malta",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "primitiveSale.plot()\n",
    "plt.show()\n",
    "sale.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "descending-secondary",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_acf(sale,lags=g_datalen-1-d).show()\n",
    "# plot_pacf(sale,lags=g_datalen/2-1-d).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "union-television",
   "metadata": {},
   "source": [
    "## 建模及预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "knowing-moisture",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0)x(0, 0, 0, 12) - AIC:93.30265108155083\n",
      "ARIMA(0, 0, 0)x(0, 0, 1, 12) - AIC:61.511542301408845\n",
      "ARIMA(0, 0, 0)x(0, 1, 0, 12) - AIC:44.90859132456146\n",
      "ARIMA(0, 0, 0)x(0, 1, 1, 12) - AIC:2.1726213092973663\n",
      "ARIMA(0, 0, 0)x(1, 0, 0, 12) - AIC:45.790959888423124\n",
      "ARIMA(0, 0, 0)x(1, 0, 1, 12) - AIC:46.70387388589718\n",
      "ARIMA(0, 0, 0)x(1, 1, 0, 12) - AIC:2.3535059484408745\n",
      "ARIMA(0, 0, 0)x(1, 1, 1, 12) - AIC:4.1726214798336425\n",
      "ARIMA(0, 0, 1)x(0, 0, 0, 12) - AIC:66.3163981572356\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 1)x(0, 0, 1, 12) - AIC:45.31426430435296\n",
      "ARIMA(0, 0, 1)x(0, 1, 0, 12) - AIC:26.29007208168307\n",
      "ARIMA(0, 0, 1)x(0, 1, 1, 12) - AIC:3.945880232344486\n",
      "ARIMA(0, 0, 1)x(1, 0, 0, 12) - AIC:27.397182396178664\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 1)x(1, 0, 1, 12) - AIC:21.618754607474155\n",
      "ARIMA(0, 0, 1)x(1, 1, 0, 12) - AIC:3.380910608197903\n",
      "ARIMA(0, 0, 1)x(1, 1, 1, 12) - AIC:5.593404952147445"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ARIMA(0, 1, 0)x(0, 0, 0, 12) - AIC:9.098776377935875\n",
      "ARIMA(0, 1, 0)x(0, 0, 1, 12) - AIC:11.508366233816673\n",
      "ARIMA(0, 1, 0)x(0, 1, 0, 12) - AIC:2.1515473976762225\n",
      "ARIMA(0, 1, 0)x(0, 1, 1, 12) - AIC:5.3894764864949565\n",
      "ARIMA(0, 1, 0)x(1, 0, 0, 12) - AIC:1.3005415756257843\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 1, 0)x(1, 0, 1, 12) - AIC:3.349340111272707\n",
      "ARIMA(0, 1, 0)x(1, 1, 0, 12) - AIC:4.548389206305968\n",
      "ARIMA(0, 1, 0)x(1, 1, 1, 12) - AIC:7.19333666122287\n",
      "ARIMA(0, 1, 1)x(0, 0, 0, 12) - AIC:6.6285731781717905\n",
      "ARIMA(0, 1, 1)x(0, 0, 1, 12) - AIC:8.624649152225885"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ARIMA(0, 1, 1)x(0, 1, 0, 12) - AIC:2.6431502265406883\n",
      "ARIMA(0, 1, 1)x(0, 1, 1, 12) - AIC:7.157133880848641\n",
      "ARIMA(0, 1, 1)x(1, 0, 0, 12) - AIC:2.727533573480173"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ARIMA(0, 1, 1)x(1, 0, 1, 12) - AIC:5.071228908693994\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 1, 1)x(1, 1, 0, 12) - AIC:5.928986644108836\n",
      "ARIMA(0, 1, 1)x(1, 1, 1, 12) - AIC:9.162315378969831\n",
      "ARIMA(1, 0, 0)x(0, 0, 0, 12) - AIC:10.290261250405521\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 0, 0)x(0, 0, 1, 12) - AIC:13.427518466911778\n",
      "ARIMA(1, 0, 0)x(0, 1, 0, 12) - AIC:2.92736834483639\n",
      "ARIMA(1, 0, 0)x(0, 1, 1, 12) - AIC:3.331165459009404\n",
      "ARIMA(1, 0, 0)x(1, 0, 0, 12) - AIC:1.9127439207813666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 0, 0)x(1, 0, 1, 12) - AIC:3.838180283805544\n",
      "ARIMA(1, 0, 0)x(1, 1, 0, 12) - AIC:3.331165440032168\n",
      "ARIMA(1, 0, 0)x(1, 1, 1, 12) - AIC:4.785733603261085\n",
      "ARIMA(1, 0, 1)x(0, 0, 0, 12) - AIC:6.592290896837611\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 0, 1)x(0, 0, 1, 12) - AIC:7.410846932906331\n",
      "ARIMA(1, 0, 1)x(0, 1, 0, 12) - AIC:2.4936481570203357\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 0, 1)x(0, 1, 1, 12) - AIC:5.9456024691924565\n",
      "ARIMA(1, 0, 1)x(1, 0, 0, 12) - AIC:3.3431416514361754\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 0, 1)x(1, 0, 1, 12) - AIC:4.291190031074933\n",
      "ARIMA(1, 0, 1)x(1, 1, 0, 12) - AIC:5.330201468989531\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 0, 1)x(1, 1, 1, 12) - AIC:7.547551325305625\n",
      "ARIMA(1, 1, 0)x(0, 0, 0, 12) - AIC:6.417984112620757\n",
      "ARIMA(1, 1, 0)x(0, 0, 1, 12) - AIC:9.840756686247595\n",
      "ARIMA(1, 1, 0)x(0, 1, 0, 12) - AIC:1.9981403959855824\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 0)x(0, 1, 1, 12) - AIC:6.61212221145858\n",
      "ARIMA(1, 1, 0)x(1, 0, 0, 12) - AIC:2.85562918889934\n",
      "ARIMA(1, 1, 0)x(1, 0, 1, 12) - AIC:4.181944631359308"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ARIMA(1, 1, 0)x(1, 1, 0, 12) - AIC:6.566001719212837\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 0)x(1, 1, 1, 12) - AIC:8.485572041462621\n",
      "ARIMA(1, 1, 1)x(0, 0, 0, 12) - AIC:8.588564548027007\n",
      "ARIMA(1, 1, 1)x(0, 0, 1, 12) - AIC:10.274502617221891\n",
      "ARIMA(1, 1, 1)x(0, 1, 0, 12) - AIC:4.609569250925367"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ARIMA(1, 1, 1)x(0, 1, 1, 12) - AIC:9.05820274035907\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 1)x(1, 0, 0, 12) - AIC:4.819692762836349\n",
      "ARIMA(1, 1, 1)x(1, 0, 1, 12) - AIC:7.042156839955473\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 1)x(1, 1, 0, 12) - AIC:8.545309942630343\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 1)x(1, 1, 1, 12) - AIC:10.90190343706552\n",
      "          pdq     pdq_x_PDQs       aic\n",
      "20  (0, 1, 0)  (1, 0, 0, 12)  1.300542\n"
     ]
    }
   ],
   "source": [
    "p =d= q = range(0, 2) \n",
    "pdq = list(itertools.product(p, d, q)) \n",
    "pdq_x_PDQs = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))] \n",
    "a=[]\n",
    "b=[]\n",
    "c=[]\n",
    "wf=pd.DataFrame()\n",
    "for param in pdq:\n",
    "    for seasonal_param in pdq_x_PDQs:\n",
    "        try:\n",
    "            mod = sm.tsa.statespace.SARIMAX(primitiveSale,order=param,seasonal_order=seasonal_param,enforce_stationarity=False,enforce_invertibility=False)\n",
    "            results = mod.fit()\n",
    "            print('ARIMA{}x{} - AIC:{}'.format(param, seasonal_param, results.aic))\n",
    "            a.append(param)\n",
    "            b.append(seasonal_param)\n",
    "            c.append(results.aic)\n",
    "        except:\n",
    "            continue\n",
    "wf['pdq']=a\n",
    "wf['pdq_x_PDQs']=b\n",
    "wf['aic']=c\n",
    "param = wf[wf['aic']==wf['aic'].min()]\n",
    "print(wf[wf['aic']==wf['aic'].min()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "exempt-consolidation",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                      SARIMAX Results                                      \n",
      "===========================================================================================\n",
      "Dep. Variable:                                sale   No. Observations:                   42\n",
      "Model:             SARIMAX(2, 1, 2)x(1, 1, [], 12)   Log Likelihood                  -1.447\n",
      "Date:                             Sat, 20 Nov 2021   AIC                             14.895\n",
      "Time:                                     17:17:19   BIC                             19.143\n",
      "Sample:                                 10-01-2017   HQIC                            14.850\n",
      "                                      - 03-01-2021                                         \n",
      "Covariance Type:                               opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.5967      2.880     -0.207      0.836      -6.242       5.048\n",
      "ar.L2         -0.0525      1.246     -0.042      0.966      -2.495       2.390\n",
      "ma.L1          0.3106   3002.399      0.000      1.000   -5884.283    5884.904\n",
      "ma.L2         -1.3107   3933.511     -0.000      1.000   -7710.851    7708.229\n",
      "ar.S.L12      -0.1735      0.286     -0.606      0.545      -0.735       0.388\n",
      "sigma2         0.0366    109.970      0.000      1.000    -215.500     215.574\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):                 1.97\n",
      "Prob(Q):                              0.86   Prob(JB):                         0.37\n",
      "Heteroskedasticity (H):               0.63   Skew:                            -0.64\n",
      "Prob(H) (two-sided):                  0.63   Kurtosis:                         4.23\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(primitiveSale, \n",
    "                                order=(2,1,2), \n",
    "                                seasonal_order=(1,1,0,12),   \n",
    "                                enforce_stationarity=False,\n",
    "                                enforce_invertibility=False)\n",
    "results = mod.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "average-office",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x238e90a1eb8>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x238e90c3c50>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x238e3e34940>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x238e90c3e48>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '宝马X3汽车销量预测')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(primitiveSale, \n",
    "                                order=(0,1,0), \n",
    "                                seasonal_order=(1,0,0,12),   \n",
    "                                enforce_stationarity=False,\n",
    "                                enforce_invertibility=False)\n",
    "results = mod.fit()\n",
    "# print(results.summary())\n",
    "fore = results.forecast(12)\n",
    "plt.plot(pd.DatetimeIndex(psale.index), psale.sale, label=\"原始数据\",color=\"b\")\n",
    "plt.plot(pd.DatetimeIndex(primitiveSale.index), primitiveSale.sale, label=\"阉割数据\",color=\"g\")\n",
    "plt.plot(fore.index, fore.values, label=\"预测结果\",color=\"r\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.title(series + \"汽车销量预测\")  # 图形标题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "american-adobe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20cfb625710>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20cfdf18c50>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20cfdf18cf8>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20cfdf18ef0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '宝马X3汽车销量预测')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fore = results.forecast(6)\n",
    "plt.plot(pd.DatetimeIndex(psale.index), psale.sale, label=\"原始数据\",color=\"b\")\n",
    "plt.plot(pd.DatetimeIndex(primitiveSale.index), primitiveSale.sale, label=\"阉割数据\",color=\"g\")\n",
    "plt.plot(fore.index, fore.values, label=\"预测结果\",color=\"r\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.title(series + \"汽车销量预测\")  # 图形标题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "naughty-analyst",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20cfdf74cf8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20cfdf9e668>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20cfdfaabe0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20cfdfd1ba8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20cfdfe8ba8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20cfe01b6a0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20cfe029b70>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20cfe05b668>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "primitiveSale.index = pd.DatetimeIndex(primitiveSale.index)\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "decomposition = seasonal_decompose(primitiveSale)\n",
    "trend = decomposition.trend #趋势效应\n",
    "seasonal = decomposition.seasonal #季节效应\n",
    "residual = decomposition.resid #随机效应\n",
    "plt.subplot(411)\n",
    "plt.plot(primitiveSale, label=u'原始数据')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label=u'趋势')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal,label=u'季节性')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label=u'残差')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stable-novel",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
